Create app.py

app.py

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+import os
+import requests
+import pandas as pd
+import plotly.express as px
+import gradio as gr
+from sklearn.preprocessing import StandardScaler
+
+# Fetch the FRED API key from environment variables
+API_KEY = os.getenv("FRED_API_KEY")
+
+def fetch_data(series_id, frequency="m", adjustment="sa"):
+    """
+    Fetch data from FRED API based on the provided series ID, frequency, and adjustment type.
+    """
+    if not API_KEY:
+        raise ValueError("FRED API key not set. Please set the FRED_API_KEY environment variable.")
+    
+    url = "https://api.stlouisfed.org/fred/series/observations"
+    params = {
+        'api_key': API_KEY,
+        'series_id': series_id,
+        'file_type': 'json',
+        'frequency': frequency,
+        'seasonal_adjustment': adjustment,
+    }
+    try:
+        response = requests.get(url, params=params)
+        response.raise_for_status()
+        data = response.json()
+        return data
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching data for {series_id}: {e}")
+        return {}
+
+def process_data(data):
+    """
+    Process the FRED data into a pandas DataFrame.
+    """
+    if 'observations' not in data:
+        print("No observations found in data.")
+        return pd.DataFrame()
+    
+    df = pd.DataFrame(data['observations'])
+    df['date'] = pd.to_datetime(df['date'])
+    df['value'] = pd.to_numeric(df['value'], errors='coerce')
+    return df
+
+def standardize_series(df):
+    """
+    Standardize the 'value' column in the dataframe using Z-scores.
+    """
+    scaler = StandardScaler()
+    df['standardized_value'] = scaler.fit_transform(df[['value']])
+    return df
+
+def create_combined_2d_visualization(dataframes, labels):
+    """
+    Generate a combined 2D line plot using Plotly.
+    Each dataframe will be plotted on the same axes.
+    """
+    combined_df = pd.concat(dataframes, keys=labels, names=['series', 'index']).reset_index(level='series')
+    fig = px.line(
+        combined_df,
+        x='date',
+        y='standardized_value',
+        color='series',
+        title="Combined Economic Data 2D Visualization",
+        labels={"standardized_value": "Standardized Value", "date": "Date", "series": "Data Type"}
+    )
+    fig.update_layout(
+        width=1200,
+        height=600,
+        xaxis_title="Date",
+        yaxis_title="Standardized Value",
+        legend_title="Data Type"
+    )
+    return fig
+
+def visualize_multiple_series(series_names):
+    """
+    Fetch, standardize, and combine multiple datasets for visualization.
+    """
+    dataframes = []
+    labels = []
+    for series_name in series_names:
+        series_id = series_options.get(series_name)
+        frequency = default_frequencies.get(series_id, "m")
+        adjustment = default_adjustments.get(series_id, "sa")
+        data = fetch_data(series_id, frequency, adjustment)
+        df = process_data(data)
+        if not df.empty:
+            standardized_df = standardize_series(df)
+            dataframes.append(standardized_df)
+            labels.append(series_name)
+    
+    if not dataframes:
+        raise ValueError("No valid data to visualize.")
+    return create_combined_2d_visualization(dataframes, labels)
+
+# Define default frequencies and adjustments
+default_frequencies = {
+    "GDP": "q",
+    "UNRATE": "m",
+    "CPIAUCSL": "m",
+    "FEDFUNDS": "m",
+    "MORTGAGE30US": "w"
+}
+
+default_adjustments = {
+    "GDP": "sa",
+    "UNRATE": "sa",
+    "CPIAUCSL": "sa",
+    "FEDFUNDS": "nsa",
+    "MORTGAGE30US": "nsa"
+}
+
+# Define options for each dropdown
+series_options = {
+    "Gross Domestic Product (GDP)": "GDP",
+    "Unemployment Rate (UNRATE)": "UNRATE",
+    "Consumer Price Index (CPI - All Urban Consumers)": "CPIAUCSL",
+    "Federal Funds Rate": "FEDFUNDS",
+    "30-Year Fixed Mortgage Rate": "MORTGAGE30US"
+}
+
+# Gradio Interface using Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("# FRED Combined Data 2D Visualizer")
+    gr.Markdown("Choose multiple economic indicators to visualize them together in a 2D space.")
+    
+    with gr.Row():
+        series_dropdown = gr.CheckboxGroup(
+            choices=list(series_options.keys()),
+            label="Select Economic Indicators to Compare"
+        )
+    
+    plot_output = gr.Plot()
+
+    # Explanation Section
+    with gr.Accordion("Color Coding Explanation", open=True):
+        gr.Markdown("""
+        - **Gross Domestic Product (GDP)**: Displayed in **blue**.
+        - **Unemployment Rate (UNRATE)**: Displayed in **green**.
+        - **Consumer Price Index (CPI - All Urban Consumers)**: Displayed in **red**.
+        - **Federal Funds Rate**: Displayed in **purple**.
+        - **30-Year Fixed Mortgage Rate**: Displayed in **orange**.
+        """)
+    
+    # Define interaction
+    series_dropdown.change(
+        visualize_multiple_series, 
+        inputs=[series_dropdown], 
+        outputs=[plot_output]
+    )
+
+demo.launch(debug=True)